Background Several approaches are available for evaluating heterogeneity in meta-
analysis. Sensitivity analyses are often used, but these are often implemented in various non …

It is generally accepted that meta-analyses should assess heterogeneity, which may be
defined as the presence of variation in true effect sizes underlying the different studies. This …

Background Many meta-analyses contain only a small number of studies, which makes it
difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis …

Meta‐analysis has become a widely used tool to combine results from independent studies.
The collected studies are homogeneous if they share a common underlying true effect size; …

Measures that quantify the impact of heterogeneity in univariate meta‐analysis, including the
very popular I2 statistic, are now well established. Multivariate meta‐analysis, where studies …

Higgins1 in his commentary on the study by Patsopoulos and coworkers2 outlines some of
the limitations of I2 in assessing heterogeneity in a metaanalysis. These limitations have …

Estimates from individual studies included in a meta‐analysis often are not in agreement,
giving rise to statistical heterogeneity. In such cases, exploration of the causes of …

Background The heterogeneity statistic I 2, interpreted as the percentage of variability due to
heterogeneity between studies rather than sampling error, depends on precision, that is, the …

Background Estimated effects vary across studies, partly because of random sampling error
and partly because of heterogeneity. In meta-analysis, the fraction of variance that is due to …

Background Assessment of heterogeneity is essential in systematic reviews and meta-
analyses of clinical trials. The most commonly used heterogeneity measure, I2, provides an …